caa a22 4500 606158928 CHVBK 20210128100622.0 cr unu---uuuuu 210128e20150101xx s 000 0 eng 10.1007/s00521-014-1703-0 doi (NATIONALLICENCE)springer-10.1007/s00521-014-1703-0 Using customer lifetime value and neural networks to improve the prediction of bank deposit subscription in telemarketing campaigns [Elektronische Daten] [Sérgio Moro, Paulo Cortez, Paulo Rita] Customer lifetime value (LTV) enables using client characteristics, such as recency, frequency and monetary value, to describe the value of a client through time in terms of profitability. We present the concept of LTV applied to telemarketing for improving the return-on-investment, using a recent (from 2008 to 2013) and real case study of bank campaigns to sell long-term deposits. The goal was to benefit from past contacts history to extract additional knowledge. A total of twelve LTV input variables were tested, under a forward selection method and using a realistic rolling windows scheme, highlighting the validity of five new LTV features. The results achieved by our LTV data-driven approach using neural networks allowed an improvement up to 4pp in the Lift cumulative curve for targeting the deposit subscribers when compared with a baseline model (with no history data). Explanatory knowledge was also extracted from the proposed model, revealing two highly relevant LTV features, the last result of the previous campaign to sell the same product and the frequency of past client successes. The obtained results are particularly valuable for contact center companies, which can improve predictive performance without even having to ask for more information to the companies they serve. The Natural Computing Applications Forum, 2014 Customer lifetime value (LTV) nationallicence Multilayer perceptron nationallicence Recency,frequency and monetary value(RFM) nationallicence Telemarketing nationallicence Bank deposits nationallicence Data mining nationallicence Moro Sérgio Department of Information Science and Technology, ISCTE - University Institute of Lisbon, 1649-026, Lisbon, Portugal aut Cortez Paulo Department of Information Systems/ALGORITMI Research Centre, University of Minho, 4800-058, Guimarães, Portugal aut Rita Paulo Business Research Unit (BRU-UNIDE), ISCTE - University Institute of Lisbon, 1649-026, Lisbon, Portugal aut Neural Computing and Applications Springer London 26/1(2015-01-01), 131-139 0941-0643 26:1<131 2015 26 521 https://doi.org/10.1007/s00521-014-1703-0 text/html Onlinezugriff via DOI BK010053 XK010053 XK010000 Metadata rights reserved Springer special CC-BY-NC licence nationallicence 1 research-article jats NATIONALLICENCE NATIONALLICENCE NL-springer NATIONALLICENCE 856 40 https://doi.org/10.1007/s00521-014-1703-0 text/html Onlinezugriff via DOI NATIONALLICENCE 700 1- Moro Sérgio Department of Information Science and Technology, ISCTE - University Institute of Lisbon, 1649-026, Lisbon, Portugal aut NATIONALLICENCE 700 1- Cortez Paulo Department of Information Systems/ALGORITMI Research Centre, University of Minho, 4800-058, Guimarães, Portugal aut NATIONALLICENCE 700 1- Rita Paulo Business Research Unit (BRU-UNIDE), ISCTE - University Institute of Lisbon, 1649-026, Lisbon, Portugal aut NATIONALLICENCE 773 0- Neural Computing and Applications Springer London 26/1(2015-01-01), 131-139 0941-0643 26:1<131 2015 26 521